The article "Artificial Intelligence in Gastrointestinal Endoscopy: A Comprehensive Review" by Hassam Ali et al. explores the significant advancements and potential of integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy. AI-enabled applications, such as computer-aided detection (CAD) and diagnosis (CADx), have significantly improved early detection, diagnosis, and personalized treatment planning for GI disorders. AI algorithms, particularly convolutional neural networks (CNNs), have shown promise in analyzing complex endoscopic data, enhancing diagnostic accuracy, and reducing the need for invasive biopsies. However, challenges such as data quality issues, overfitting, and operator-dependent accuracy persist, necessitating further research and validation. The review highlights the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly in early detection and personalized treatment of GI diseases, including esophageal disorders, gastric precancerous lesions, H. pylori infection, gastric cancer, colorectal polyps and cancer, inflammatory bowel disease (IBD), and pancreato-biliary diseases. Despite these advancements, the integration of AI into routine clinical practice requires addressing limitations such as data standardization, ownership, and protection, and fostering collaboration between researchers, doctors, and AI professionals.The article "Artificial Intelligence in Gastrointestinal Endoscopy: A Comprehensive Review" by Hassam Ali et al. explores the significant advancements and potential of integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy. AI-enabled applications, such as computer-aided detection (CAD) and diagnosis (CADx), have significantly improved early detection, diagnosis, and personalized treatment planning for GI disorders. AI algorithms, particularly convolutional neural networks (CNNs), have shown promise in analyzing complex endoscopic data, enhancing diagnostic accuracy, and reducing the need for invasive biopsies. However, challenges such as data quality issues, overfitting, and operator-dependent accuracy persist, necessitating further research and validation. The review highlights the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly in early detection and personalized treatment of GI diseases, including esophageal disorders, gastric precancerous lesions, H. pylori infection, gastric cancer, colorectal polyps and cancer, inflammatory bowel disease (IBD), and pancreato-biliary diseases. Despite these advancements, the integration of AI into routine clinical practice requires addressing limitations such as data standardization, ownership, and protection, and fostering collaboration between researchers, doctors, and AI professionals.